K3S Cluster on Jetson Nano

Jetson Nano Development Board

Setting up a Kubernetes Cluster on Jetson Nano (with k3s)

The Jetson Nano is an easily accessible, yet powerful single board computer built to deploy machine learning applications and more. Kubernetes is the most popular orchestration system used to manage and automate your application deployment, through a Kubernetes Cluster. K3s is the more lightweight version of Kubernetes.

This week we look at setting up a Kubernetes cluster on two Jetson Nanos, although you can do it with as many worker nanos as you’d like. It can be tricky to do, especially with no guide that outlines how to do it specifically for the Jetson Nano’s unique architecture. Although there are many other guides out there, this one is specifically for the Nano and will address any specific issues that come with that.

What We Will Use:

  • 2 fresh Jetson Nanos running Ubuntu 18.04, with Jetpack SDK 4.5 installed.

Preliminary Steps:

The first thing we need to decide is which jetson will be our master node, and which one(s) will be our worker nodes. The master node is the nano that you will deploy the cluster from, and the worker node(s) will join the cluster. Name them accordingly. I have named mine master and node1.

Then, use SSH to work on all the Nanos easily. Use this:

ssh user@ <target ip address>

And then login as you normally would on the nano you are SSHing into.

We will need curl for this, so Use this to install curl on all nanos.

sudo apt-get install curl

To make things easier, I recommend running “sudo su” to avoid having to type sudo before everything.

1. Installing Master Node:

We will now configure the Master Node. On your master nano only, run:

curl -sfL https://get.k3s.io | sh -s - --no-deploy traefik --write-kubeconfig-mode 644 --node-name k3s-master-01

 This installs k3s and starts it, deploys a cluster, and sets this node as the master.

You can view that your master node is online by running:

kubectl get nodes

You should see your “k3s-master-01” node is the only one in the cluster.

For the next step, which is installing the worker nodes, we will need the master node’s token. To get it, run this:

cat /var/lib/rancher/k3s/server/node-token

And copy the token for the next step.

2. Installing Worker Node(s):

We will now configure the Worker Nodes. Do this on all the worker nodes you have.

curl -sfL https://get.k3s.io | K3S_NODE_NAME=k3s-worker-01 K3S_URL=https://<IP>:6443 K3S_TOKEN=<TOKEN> sh -

Replace <IP> and <TOKEN> with the master node’s ip address (you can get this by running ifconfig) and the token you previously saved.

Now, when you run “kubectl get nodes” on the master node, you can see that the worker has joined.

3. Bringing up the dashboard

At this step, you’re pretty much done, your cluster is up, and you can begin deploying containers. I will show you now how to bring up the dashboard to view all your containers once they are deployed.

First, run this on the master node. This will deploy the kubernetes dashboard.

GITHUB_URL=https://github.com/kubernetes/dashboard/releases
VERSION_KUBE_DASHBOARD=$(curl -w '%{url_effective}' -I -L -s -S ${GITHUB_URL}/latest -o /dev/null | sed -e 's|.*/||')
sudo k3s kubectl create -f https://raw.githubusercontent.com/kubernetes/dashboard/${VERSION_KUBE_DASHBOARD}/aio/deploy/recommended.yaml

Now we have to create a few files:

  • Dashboard.admin-user.yml (do vim dashboard.admin-user.yml), press i to enter insert mode, and paste the following.
apiVersion: v1
kind: ServiceAccount
metadata:
  name: admin-user
  namespace: kubernetes-dashboard

Press esc then :x to exit the vim editor and save.

  • Dashboard.admin-user-role.yml (do vim dashboard.admin-user-role.yml), press i to enter insert mode, and paste the following.
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: admin-user
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: cluster-admin
subjects:
- kind: ServiceAccount
  name: admin-user
  namespace: kubernetes-dashboard

Save the file the same way as the previous one.

Now we will deploy the admin-user configuration. Run:

k3s kubectl create -f dashboard.admin-user.yml -f dashboard.admin-user-role.yml

Now we will access the token needed to access the dashboard locally in a web browser. Run:

k3s kubectl -n kubernetes-dashboard describe secret admin-user-token | grep '^token'

And keep note of the very long token.

Now we will create a secure channel to the cluster. To do this, run:

k3s kubectl proxy

You should see:

Starting to serve on 127.0.0.1:8001

This means that the dashboard is being served at 127.0.0.1, on port 8001. At this link you will find your dashboard:

http://localhost:8001/api/v1/namespaces/kubernetes-dashboard/services/https:kubernetes-dashboard:/proxy/

It will prompt you for the token we copied in the previous step. Paste it here, and you will have access to the kubernetes dashboard.

And you’re done! Once you deploy containerized apps, you will be able to see and manage them in the dashboard.

Other useful commands

To shut off your cluster, run:

k3s-killall.sh

To delete your dashboard, run:

sudo k3s kubectl delete ns kubernetes-dashboard
sudo k3s kubectl delete clusterrolebinding kubernetes-dashboard
sudo k3s kubectl delete clusterrole kubernetes-dashboar
d

To restart the cluster later, run:

sudo systemctl restart k3s

In next week’s blog post, we will look at containerizing apps and deploying them. We will also look at managing them between nodes, and using the dashboard more.

What is Edge AI?

What is Edge AI and How Does It Create a New Paradigm

Edge computing diagram
IEEE Edge Diagram

Artificial intelligence and machine learning (AI/ML) have been around for a while now. Without knowing the intricacies, people still understand that it has powerful uses, such as analyzing data, understanding pictures, or chatting with Siri. The edge is a much newer concept, which I first heard of with the development of 5G.

The edge is data processing with round trip servicing under 20 milliseconds. What does this actually mean? A more practical definition is self-explanatory. The edge is literally the closest point you can put your processing to where you need the result. This leaves a broader concept of edge. If your processing must be done in the cloud, then your edge is simply the closest/fastest servers. If your processing can be done locally, then your edge should be your local processor (smartphone?).

But what do you get when you marry the AI and edge concepts? At one point of time, AI algorithms became practical to implement in the cloud, running on powerful server racks. Since AI was too complex for local devices, the cloud AI was also the edge AI. This is no longer true. Powerhouses such as #arm and #NVIDIA have been making massive breakthroughs in optimizing AI performance. Startup #Hailo has developed their own AI processor. #STM has even put ML cores on individual sensors. AI can now run on embedded processors, sensors, internet gateways, security cameras, etc. The new edge AI is essentially anywhere.

Believe it or not, this new reality is a great thing. Since data is generated and processed locally, the edge AI device does not need to be connected to the internet. This means you can have smart devices that protect your privacy. You can have security systems that cannot be taken down by cutting the internet. AI analytics in remote areas? Got it. Edge enables AI to be anywhere it is useful, when it is useful.

#Millspaw Electronics is launching an edge AI brand, Normal AI. Be sure to connect to learn what we can do for you.